Prof. Sarit K. Das Prof. Sarit K. Das Department of Mechanical Engineering Department of Mechanical Engineering Indian Institute of Technology Madras Indian Institute of Technology Madras Experimental and Experimental and Modeling Skills Modeling Skills
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Prof. Sarit K. DasProf. Sarit K. DasDepartment of Mechanical EngineeringDepartment of Mechanical Engineering
Indian Institute of Technology MadrasIndian Institute of Technology Madras
Experimental and Modeling Experimental and Modeling SkillsSkills
Can/should research methodology / experimental skills be taught by giving
lectures?
No, because These are arts and experiences. They are diverse.
Yes, because Even art has a grammar. There are commonalities. It serves as a guidance to the research
scholar.
• “When you do not know what you are doing, do it neatly.”
• “Team work is essential. It always allows you to blame someone else.”
• “If reproducibility may be a problem, conduct the test only once.”
• “If a straight line fit is required, obtain only two data points.”
• “If an experiment works, something has gone wrong.”
• “If enough data is collected, anything may be proven by statistical methods.”
Jokes about Experiments
Why should we do experiments at all?
• To know the truth.
• To get insight into the physics.
• To determine the unknown factors
responsible.
• To test a theory.• To facilitate simulation/modeling work. (eg. use of experimentally determined
unknown constants in turbulence modeling)
• Defining aim/objective of the experiments (but remain flexible)
• Scope of the work (research is infinite but requirement of a
research degree is limited – Dr. Sahoo)
• Defining the parameters you are looking at.
• Broadly decide the approach (e.g., theoretical, experimental, both,
numerical)
Planning
• Working out the best possible design with all the constraints you have got.
• Inputs from literature and suggestions from the technicians.
• Changing design at a later stage may be very costly in terms of time and money.
• In case of a new design, trial and error method is the only option.
(eg. Heater-thermocouple design.)
Design
• Making thorough enquiries to find a potential fabricator/supplier with good expertise in the relevant area.
• Following proper order in steps/stages involved in fabrication with parallel efforts so as to minimize the time.
• Maintaining the healthy relationship with the fabricator to get the best out of him. Respect the technicians, you can learn from them what you cannot learn from your Professor.
Fabrication
Points to remember in instrumentation
While choosing the instruments, attention has to be paid to:
•Accuracy•Precision •Range•Response time •Least count•Cost
Calibration and error analysis
• Principles of transducers.
• Calibration of the equipment with a standard device.
• Systematic errors and random errors (Lord Rayleigh).
• Number of variables and error estimation principle statistically.
• A realistic approach towards accuracy.
Validation of the experimental set-up and results with benchmark results from literature.
Running the experiments
• Before running the experiments, make sure that all the components/ connections/loops/circuits are proper.
• Make a measurement protocol with date and specific conditions. Do not forget to specify atmospheric conditions if they are important.
• Carry out the experiments according to the laid-out procedure.
• Monitor all the components and devices for possible fluctuations/deviations from the set values while experiment is in progress.
• After few experiments try to explain your results and then proceed further.
Safety measures
Do not compromise on safety aspect. All the possible precautions are to be taken. Some of them are:
•Goggles/Masks/Helmets/Gloves/Apron while doing experiments with poisonous/ hazardous chemicals, applying silicone sealants, dealing with extremely fine powder.
•Safety valves/ safety meshes while doing high pressure glass vessel experiments. (Prof. RN)
•Sound guards in case of wind tunnel experiments. (Prof at ME).
•Utmost care (shock-proof gloves and shoes) while doing high voltage experiments.
•Fire extinguisher a must for all experiments.
•Care and ethical issues in handling biological samples and living animals.
Analysis of the experimental results
• Sufficient number of data points. “ Between three data points, any curved
profile can be fit”
• Consistency and repeatability (precision).
• Accuracy
• Look at the results in unbiased way or with out any prejudice. (eg. Nano-boiling)
….contd.
• Keen observation of the data. (eg. Becquerel) …. experiments with frogs
(Galvany) etc….
• Honesty- Don’t ‘MOVE’ your experimental points so as to match with theory. Leave them as they are. An explanation may be there.( Positron at Saha Institute,Fynmann)
• Changing the course of the experiments depending on the results.
Documentation/communication of the results
• All the results are to be documented irrespective of the nature/use as these may be of use later. (eg. Pressure drop -Prabhakar).
• Communicating the results to journals/conferences is not just for publication, but also to get comments/ suggestions/reviews from international community which will be of much help in your research.
Mental Frame
• Pride and modesty• Avoid frustration have patience and
perseverance. Never give up (Edison).• Be relaxed and appreciate difficulties
(Heater).• Stay focused (Arjuna).• Be flexible in approach (Frank and me- temp.
measurement).• Be honest (Fermi / IISc. Scholar).• Always have a mental picture of your
experiments going right (even in dream or toilet).
• Avoid postponement.
……some more
• Discipline, sincerity, punctuality but freedom and informality promotes creativity.
• Interaction with your friends – Help each other without jealousy/envy.
• Be fully prepared for the unforeseen problems.• Requires few months to few years of
preparation to get results in no time.• It is in journey or in efforts that true
satisfaction/happiness lies, not in reaching goal.
Besides helping you in getting publications, degree, and a job,
what do experiments teach you in life?
Skill of planning. Hope. Patience. Art of management. Broad outlook. Pleasure of getting to a truth
Mathematical ModelingMathematical Modeling
Why mathematical modeling?
• To understand the behaviour of systems• To obtain optimal designs/ operational
conditions• To analyse critical or failure scenarios
where experiments are not possible (Nuclear / Rockets etc.,)
• To obtain variation of parameters in details like the complete field (flow /thermal/electric)
Modeling Methods
• Order-of-magnitude & Dimensional analysis
• Overall system level models (Steady/
transient) Lumped/Assembly (Fuel cells)
• Simplified 1-D/ 2-D models
• Fully comprehensive transient, 3-D models
• Heuristic /statistical models
Types of Solutions
• Closed form analytical solutions
• Asymptotic solutions (perturbation solutions)
• Numerical solutions
• Heuristic solutions
Example- understanding phenomena• Failure of Takoma bridge
• The vortex shedding frequency of wind flow matched with the natural frequency of the suspension bridge
• Failure occurred by torsional vibrations - resonance with vortex shedding
• It is possible to carry out a detailed dynamic anaylsis of the bridge structure using FEM and the natural frequencies can be predicted
Example- Optimisation
• Horizontal distance x = (Vo cosa)t
• When projectile hits the ground, y = 0. In other words, tmax = (2Vo sina)/g
• The maximum horizontal distance travelled = xmax = (Vo
2 sin2a)/g
For given Vo the range is maximum for a = 45o
•Consider a projectile with initial velocity Vo and projected angle ‘a’
•Vertical distance travelled y = (Vo sina)t - ½ gt2
Order of Magnitude Analysis
• Consider a water drop of 1 mm diameter falling in air. We want to get the terminal velocity of the drop
• At terminal velocity,drag on drop = weight of the drop
• Drag force = CD.(½ rair V2). pd2/4• Weight = rwater.(pd3/6).g
• Taking rair= 1 kg/m3, rwater = 1000 kg/m3 , g = 10 m/s2 and CD= 0.5, we get V = 2.58 m/s. This is a typical velocity for a falling droplet
Drag force = CD.(½ rair V2). pd2/4
Weight = rwater.(pd3/6).g
Order of Magnitude Analysis
• If we want to see whether a falling drop of 1 mm will be distorted, the analysis can be done as follows.
• Inertial force ~ drag force ~ (½ rair V2). pd2/4• Surface tension force ~ (4s/d). Pd2
• The non-dimensional number called Weber number We = (rair V2d/s) = (inertia/ surface tension) determines whether a drop will be distorted from spherical shape or not.
Dimensional Analysis
• This helps in constructing laboratory scale models which can simulate the situation of the actual prototype
• By testing an aircraft model at the same Reynolds number and Mach number as the actual prototype, wind tunnel studies can give valuable data for the actual aircraft
• Reynolds number = (inertial force/ viscous force)• Mach number = (fluid velocity/ velocity of sound).
The Mach number characterizes the effects due to compressibility in a flow.
• With the help of non- dimensional parameters, a large number of experimental data can be reduced and correlated in terms of the dimensionless numbers
• For example, the drag coefficient CD = f(Re) for any low speed flow over an object of given shape
Dimensional Analysis
Renowned British scientist G.I. Taylor was told by the British government about the development of the atomic bomb and was asked to think about the mechanical effect produced by such an explosion. Taylor using dimensional analysis gave the radius of the shock wave at any time t as
1/52/5E
R C t
Dimensional Analysis
System Level Simulation
• For a mass-spring- damper system, the governing equation is
• Similarly, for an electrical system (inductor L, resistance R and capacitor C)
)(tFkxxBxm
)(1
tEqC
qRqL
• The governing equations can be solved exactly for simple linear systems and the dynamic behaviour of the system can be predicted
• The frequency of oscillations for the system and the possibility of resonance leading to system instability can be predicted
System Level Simulation
Computational Modeling
• Computers and numerical techniques have witnessed phenomenal growth
• Compared to experimental analysis which may be expensive, computational simulation has become an economic alternative
Applications of computational modeling
• Aerospace vehicles
• Automobiles
• Power generation
• Manufacturing technology
• Structural designs
• Environmental pollution
Steps involved in modeling
• Creation of the geometry
• Division of the geometry into a computational mesh
• Application of mass balance, force balance and energy balance principles to small computational cells
• Solution of variables such as velocity, pressure, density, temperature, stresses, displacements etc. At various points in the geometry
• Pre-processing (creation geometry & mesh and application of boundary conditions)
• Analysis (solution of governing equations)
• Post-processing (estimation of desired quantities- say, heat transfer from prediction of temperature, etc.)
Steps involved in modeling
Aircraft Model
Launch vehicle
Modeling Methods
• Finite Difference Method
• Finite Volume Method
• Finite Element Method
• Boundary Element Method
• Spectral Method
Artificial Intelligence Tools
• Many non-deterministic search methods are available for the development of expert systems and artificial intelligence tools
• These search methods have great utility for the solution of practical engineering problems which are difficult to solve because of the enormity, complexity, non- linearity and uncertainty involved. They provide quite useful solutions with very little effort.
Non-deterministic search methods
• Fuzzy sets and Fuzzy logic
• Artificial Neural Networks
• Genetic Algorithms
• Simulated Annealing
Illustrations
Modeling of biological heat transfer
Thermal profile of tumor (laser ablation): experimental and model
Photothermal Tumor Destruction
Thermal profile of tumor (magnetic ablation) : model
Thermal history of tumor : experimental and model
Modeling of Two-Phase Flow
Rayleigh -Taylor Instability
Transient Evolution of Film
0
β0 = 1.26
0
β0 = 2.63
Local film thicknessβ =Fully developed film thickness
Non dimensional film thickness,
Liquid Falling Film on an Inclined Surface
Before you begin modeling…..
• Understand the limitations – “as the model so are the solutions”. Model can simulate only the physics that you have considered through mathematical equations (channel vs micro-channel and surface tension).
• Beware of your assumptions and boundary conditions (continuum / no-slip / constant property etc.,)
• Cannot replace experiments fully. Can only reduce the number of experiments.
• Always look for proper validation and accuracy
What is important in research?
• Whether you are doing experiment or modeling your aim is to reveal THE TRUTH not some degree or publication. Those you get incidentally.
• The method is not more important than the problem unless you are developing the method itself.
• Always look for novelty in research. Analysis is important to prove an idea but the idea is more important than that. Hence spend more time at your table than computer terminal or laboratory.
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